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分析claude-flow v3的MCP服务器延迟与负载，协助实现连接复用、工具检索、负载均衡和通信改进，并监控响应时间、错误率、内存及缓存表现。

• 调查MCP启动或响应缓慢的原因
• 复用连接并清理闲置连接
• 改进工具检索与请求分配

根据任务类型将后台工作分配给专业智能体，利用执行历史中的质量评分、成功率、平均响应时间和执行次数辅助选择负责人，并查看性能及协作统计。

• 按任务类型选择专业智能体
• 查看质量、成功率和响应时间
• 用执行反馈辅助后续分配

将软件开发划分为需求、设计、测试先行的实现、审查和部署等阶段，通过Claude Flow协调多个智能体分工，管理开发全过程。

• 从需求出发开发新功能
• 结合测试改造现有代码
• 排查故障并添加回归测试

Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, scan for security issues, and manage releases across repositories.

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• Create workflows for automated testing and deployment
• Investigate failed runs and suggest fixes
• Coordinate code reviews and security checks

Work with AI as a coding partner, choosing who writes code and who guides the work. Supports role switching, test generation, ongoing code review, debugging, and step-by-step explanations for learning.

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• Write code with ongoing AI review
• Direct AI implementation while reviewing its output
• Develop features by writing tests first

Analyze teams of agents in Claude Flow to find delays in communication, processing, memory, and network access. Generate performance reports, compare runs, and review recommendations or apply available automatic optimizations.

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• Investigating slow agent tasks
• Creating weekly performance reports
• Comparing performance before and after changes

Implement agents that use recorded task outcomes to choose future approaches. ReasoningBank supports matching past patterns, comparing strategies, retaining experience in AgentDB, and tracking how strategy success and learning change over time.

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• Choose task strategies using past outcomes
• Compare approaches to code review or bug fixing
• Match current situations to learned patterns

Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.

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• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents

Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.

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• Compare performance before and after v3 optimization
• Evaluate vector search speed and memory use
• Track performance regressions over time

Turn a feature request into a minimal implementation plan before coding. Identify which files need changes, explain why, define the smallest required interface, and flag broader effects or expanded permissions for the implementer.

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• Plan a feature before coding
• Identify files affected by a change
• Review scope and permission changes

Build custom Claude Code skills with clear instructions, reusable templates, and organized supporting files. The skill guides you through YAML metadata, meaning the skill’s name and description, and separates essential steps from detailed references.

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• Turning recurring workflows into custom skills
• Creating skill templates with supporting scripts
• Organizing long instructions into reference files

Coordinate specialist agents in Claude Flow under a lead agent. Distribute development or research tasks, use voting to make decisions, preserve shared knowledge, and pause or resume work through managed sessions.

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• Splitting application development among specialist agents
• Comparing technologies through research and voting
• Coordinating code reviews across specialties

Review and strengthen security in claude-flow v3 by addressing dependency risks, password hashing, credential generation, input validation, file paths, and command execution. It also guides threat modeling and tests for security-critical code.

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• Reviewing security boundaries and potential threats
• Updating dependencies and checking audit results
• Revising password hashing and credential generation

Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.

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• Turn code analysis into an improvement plan
• Prioritize and refactor existing code
• Find test gaps and generate unit tests

Build agent memory with ReasoningBank and AgentDB. Record actions and outcomes, retrieve similar experiences, assess results, and consolidate memories into reusable patterns so agents can draw on past work when making decisions.

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• Build agent memory that reuses past experiences
• Record and assess sequences of agent actions
• Consolidate similar experiences into reusable patterns

Improve the MCP server that connects claude-flow v3 to tools. It guides connection reuse, faster tool lookup, request distribution, and communication changes, with monitoring to evaluate response times, errors, memory use, and cache performance.

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• Analyze slow MCP startup and tool responses
• Reuse connections and clean up idle ones
• Improve tool lookup and distribute requests

Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.

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• Build answers grounded in retrieved documents
• Add meaning-based search to a knowledge base
• Find documents with similar content

Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.

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• Sharing stored patterns across multiple AgentDB instances
• Filtering semantic search by date or category
• Separating knowledge and conversation databases

Create background workers that analyze code, detect exposed secrets, learn code patterns, or generate API documentation. Combine predefined processing stages or start from presets, configure target files in YAML, and inspect worker status and analysis results.

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• Scan authentication code for security issues and exposed secrets
• Generate API documentation from endpoints and types
• Analyze code complexity and dependencies

Manage GitHub issues, synchronize project boards, and plan sprints, short development cycles. Coordinate multiple AI agents to break work into tasks, assign responsibilities, update progress, and produce reports on completion and blockers.

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• Sorting and prioritizing incoming GitHub issues
• Splitting large issues among AI agents
• Synchronizing task progress with project boards

Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.

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• Splitting large command files into focused modules
• Adding interactive setup prompts
• Recording command results through execution hooks

Improve the code that plans, reviews, and controls agent work while keeping the model fixed. Darwin Mode tests changes in isolated environments and retains variants that pass tests and show measurable improvement.

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• Testing changes to planning and review logic
• Evaluating retry, tool, and memory policies
• Trying an offline dry run

Restructure claude-flow v3 into focused modules using Domain-Driven Design, which groups code by responsibility. Define module boundaries, event communication, and plugin interfaces to support a modular architecture with independently testable business logic.

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• Split an oversized orchestrator into focused modules
• Define boundaries between tasks, sessions, and monitoring
• Design plugin extensions and domain events

Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

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• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues